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Construction of risk factors and prediction model for thrombosis in arteriovenous graft
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Yumei Fang1,2,
Autor para correspondencia
yummyla@163.com

Corresponding author: Department of Nephrology, The First People’s Hospital of Hangzhou Lin'an District, 360, Yikang Road, Jinnan Street, Lin'an District, Hangzhou City, Zhejiang Province, 311300 China
, Xia Cao1,3
1 Department of Nephrology, The First People’s Hospital of Hangzhou Lin'an District, China
2 Department of Nephrology, The First People’s Hospital of Hangzhou Lin'an District, 360, Yikang Road, Jinnan Street, Lin'an District, Hangzhou City, Zhejiang Province, 311300 China
3 Department of Nephrology, The First People’s Hospital of Hangzhou Lin'an District, 360, Yikang Road, Jinnan Street, Lin'an District, Hangzhou City, Zhejiang Province, 311300 China
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Abstract

Objective: This study aims to identify risk factors for thrombosis in arteriovenous grafts and construct a predictive model to assess thrombosis risk in patients undergoing maintenance hemodialysis (MHD).

Methods: A total of 160 MHD patients with arteriovenous graft were included and divided into a thrombosis group (n=39) and a control group (n=121). Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. A nomogram prediction model was developed using R software, and its predictive performance was evaluated through calibration curves and C-index validation.

Results: Multivariate analysis identified diabetes, hypotension during dialysis, arteriovenous graft stenosis, compression hemostasis >30 min, and calcium-phosphorus product >55 mg²/dL² as independent risk factors for arteriovenous graft thrombosis. The nomogram model demonstrated good predictive accuracy, with an initial C-index of 0.753 and a validated C-index of 0.735.

Conclusion: The established nomogram effectively predicts arteriovenous graft thrombosis risk, aiding early identification and targeted intervention for high-risk patients.

Keywords:
artificial vascular arteriovenous thrombosis
thrombus
maintenance hemodialysis
risk factors
prediction models
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Nefrología
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